Optical character recognition is to detect and recognize printed characters by electronic equipment. With the automation degree and intelligent degree of social production and livelihood becoming higher, OCR is more widely applied, such as a printing detection system on various packages, a plate number positioning and character recognition system in intelligent transportation, a crown word number recognition in banknote recognition, a serial number recognition in bill recognition and layout analyses in bill recognition. Therefore, developing an efficient optical character recognition system has great social benefits and economic benefits.
In practical applications, because of non-consistency of shooting scenes of an image and influences of various factors, such as sensors and illuminations, situations of complex image backgrounds, such as shading, seals and patterns always occur. A bottleneck of OCR is no longer a design of a classifier but mainly depends on the accuracy of character segmentation, and in particular to segmentation of adhered or broken character lines. Therefore, a more efficient character segmentation method should be proposed.
At present, a general character segmentation method is an image-based segmentation method, which includes regarding an initial point of an image as a candidate segmentation point, determining other effective segmentation points and screening a target segmentation point from the effective segmentation points. The method is to obtain the target segmentation point by using properties of a single character and to recognize a segmented character. However, the segmentation method has a low character recognition capability and a bad anti-dirty capability under a complex background.